Digital Image Processing- Enhancement

DIGITAL IMAGE PROCESSING
IMAGE ENHANCEMENT
 Image Enhancement is the process of making an image more
interpretable for a particular application(Faust1989).
 Understanding of imagery Enhancements are used to make it
easier for visual interpretation and.

 The advantage of digital imagery is that it allows us to
manipulate the digital pixel values in an image.

 Although radiometric corrections for illumination,
atmospheric influences, and sensor characteristics may be
done prior to distribution of data to the user, the image may
still not be optimized for visual interpretation.

 Remote sensing devices, particularly those operated from
satellite platforms, must be designed to cope with levels of
target/background energy which are typical of all conditions
likely to be encountered in routine use.

 With large variations in spectral response from a diverse
range of targets (e.g. forest, deserts, snowfields, water, etc.)
no generic radiometric correction could optimally account for
and display the optimum brightness range and contrast for all
targets.

 Thus, for each application and each image, a custom
adjustment of the range and distribution of brightness values
is usually necessary.

 Image Enhancement Algorithms are applied to Remotely
Sensed data to improve the appearance of an image for
human visual analysis or for subsequent machine analysis.
 There is no such thing as ideal or best Image Enhancement
because the results are ultimately evaluated by humans who
make subjective judgement as to whether a given Image
Enhancement is useful.
The techniques to be used in Image Enhancement depend upon
1. The user’s data
2. The user’s Objective
3. The user’s Expectation

4. The user’s background

Image Enhancement Techniques can be grouped under
following three categories :
 RADIOMETRIC ENHANCEMENT – Enhancing
images based on the values of individual pixels (Point
Operation).
 SPATIAL ENHANCEMENT - Enhancing images
based on the values of individual and Neighbouring
Pixels (Local Operation).
 SPECTRAL ENHANCEMENT – Enhancing Images
by transforming the values of each pixel on a
multiband basis.

RADIOMETRIC ENHANCEMENT
Linear

Non Linear

Minimum- Maximum

Logrithmic

% Linear

Inverse Log

Piece Wise Linear

Exponential
Square
Square Root
Cube
Cube Root
Arc Tangent
Histogram
Equalization

Histogram
Matching

CONTRAST

 The range of brightness values present on an image is
referred to as contrast.

 Remote Sensors record reflected and emitted Radiant Flux
exiting from the Earth’s surface material.

 Ideally one material would reflect a tremendous amount of
energy in a certain wavelength, while another material would
reflect much less energy in the same wavelength.

 A common problem in remote sensing is that the range of
reflectance values collected by a sensor may not match the
capabilities of the film or color display monitor.

FACTORS LEADING TO LOW CONTRAST IN
REMOTELY SENSED IMAGE

 Different materials often reflect similar amounts of
radiant flux throught out the Visible, NIR and MIR
portion of EM Spectrum.

 Cultural Factors eg. People in developing countries
use natural building material( wood, soil) in
construction of urban areas.

 Sensitivity of the Detector – Detectors on most
sensing systems are designed to record a relatively
wide range of scene brightness values(0-255) without
becoming saturated. However very few scenes are
composed of brightness values that utilize the full
sensitivity range of the detectors.

CONTRAST ENHANCEMENT

 Contrast enhancement is a process that makes the image
features stand out more clearly by making optimal use of the
colors available on the display or output device.

 Contrast manipulations involve changing the range of values
in an image in order to increase contrast.

 For example, an image might start with a range of values
between 40 and 90. When this is stretched to a range of 0 to
255, the differences between features is accentuated.

 The contrast of an image can be increased by utilizing the
entire brightness range of a display or hard-copy output
device.

 Digital methods generally produce a more satisfactory
contrast enhancement because of the precision and wide
variety of processes that can be applied to the imagery.

 Linear and nonlinear digital techniques are two widely
practiced methods of increasing the contrast of an image.

LINEAR CONTRAST STRETCHING

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Linear contrast enhancement, also referred to as a contrast
stretching, linearly expands the original digital values of the
remotely sensed data into a new distribution.

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By expanding the original input values of the image, the total
range of sensitivity of the display device can be utilized.

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Linear contrast enhancement also makes subtle variations
within the data more obvious.

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These types of enhancements are best applied to remotely
sensed images with Gaussian or near-Gaussian histograms,
meaning, all the brightness values fall within a narrow range
of the histogram and only one mode is apparent.

There are three methods of linear contrast enhancement:





Minimum-Maximam Linear Contrast Stretch
Percentage Linear Contrast Stretch
Piecewise Linear Contrast Stretch

Minimum-Maximum Linear Contrast Stretch
 The original minimum and maximum values of the data are
assigned to a newly specified set of values that utilize the full
range of available brightness values.
 Consider an image with a minimum brightness value of 84
and a maximum value of 153. When such an image is viewed
without enhancements, the values of 0 to 83 and 154 to 255
are not displayed.
 Important spectral differences can be detetected by stretching
the minimum value of 83 to 0 and the maximum value of 153
to 255

 An algorithim can be used that matches the old minimum
value to the new minimum value, and the old maximum
value to the new maximum value.

 All the old intermediate values are scaled proportionately
between the new minimum and maximum values.

 Many digital image processing systems have built-in
capabilities that automatically expand the minimum and
maximum values to optimize the full range of available
brightness values.

fig: Linear Contrast stretching

LINEAR CONTRAST STRETCHING

RAW DATA

MINIMUM-MAXIMUM CONTRST ENHANCEMENT

Percentage Linear Contrast Stretch
 The percentage linear contrast stretch is similar to the
minimum-maximum linear contrast stretch except this
method uses a specified minimum and maximum values that
lie in a certain percentage of pixels from the mean of the
histogram.
 A standard deviation from the mean is often used to push the
tails of the histogram beyond the original minimum and
maximum values.
 It is not necessary that the same percentage be applied to
each tail of the histogram distribution.

 An analyst wanting to extract detailed marine information in
an image may only be interested in values between 0 and 12.
When these values are stretched to 0 and 255, subtle ocean
variations can be more easily detected .

 A percentage stretch of the same image between values of 25
and 45 yields detailed vegetation information. This may be
useful in the delineation of healthy vegetation.

 If an analyst is interested in image enhancement for urban
features, a percentage linear stretch between the values 40
and 70 in the red gun and 15 to 45 in the green and blue guns
will increase the contrast of these features.

Percentage Linear Contrast Stretch

(a) Data without Enhancements

(b) Enhanced for Ocean

Bands 4,3, and 2

Contrast
Red 0-10 Green 0-12 Blue 012

(c) Enhanced for Urban
(b) Enhanced for Vegetation
Contrast
Contrast
Red 40-70 Green 15-45 Blue
Red 25-45 Green 30-35 Blue 25-30
15-45
Percentage Linear Stretch

Piecewise Linear Contrast Stretch
 When the distribution of a histogram in an image is bi or
trimodal, an analyst may stretch certain values of the
histogram for increased enhancement in selected areas.
 This method of contrast enhancement is called a piecewise
linear contrast stretch.
 A piecewise linear contrast enhancement involves the
identification of a number of linear enhancement steps that
expands the brightness ranges in the modes of the histogram.
 In the piecewise stretch, a series of small min-max stretches
are set up within a single histogram.
 Because a piecewise linear contrast stretch is a very powerful
enhancement procedure, image analysts must be very familar
with the modes of the histogram and the features they
represent in the real world.

Piece wise linear stretch

NON LINEAR CONTRAST STRETCH
 DN values are non linearly stretched to yield an enhanced
image.
 Output brightness value is a function of input brightness
value.

 Increases contrast in different regions of histogram
 There are basically two types of Non linear enhancement
techniques :
 MATHEMATICAL - log, inverse log,square,
square root, cube, cube root etc.
 STATISTICAL – Histogram Equalization etc.

Logrithmic Stretch
 In this process the logarithmic values of the input data are
linearly stretched to get the desired output values.
 It is a two step process. In the first step we find out the log
values of the input DN values. In the second step the log
values are linearly stretched to fill the complete range of DN
no. (0-255).
 Logrithmic stretch has greatest impact on the brightness
values found in the darker part of the histogram

Histogram Equalization
 Histogram equalization is one of the most useful forms of
nonlinear contrast enhancement.
 When an image's histogram is equalized, all pixel values of
the image are redistributed so there are approximately an
equal number of pixels to each of the user-specified output
gray-scale classes (e.g., 32, 64, and 256).
 Contrast is increased at the most populated range of
brightness values of the histogram (or "peaks").
 It automatically reduces the contrast in very light or dark
parts of the image associated with the tails of a normally
distributed histogram (Jensen 1996).
 Histogram equalization can also separate pixels into distinct
groups, if there are few output values over a wide range.

HISTOGRAM EQUALIZED IMAGE

 Enhanced image gains contrast in peaks

 Data values in the tails of the original histogram are grouped
together so contrast among tail pixels is lost.

 Image analysts must be aware that while histogram
equalization often provides an image with the most contrast
of any enhancement technique, it may hide much needed
information.

 This technique groups pixels that are very dark or very bright
into very few gray scales.

 If one is trying to bring out information about data in terrain
shadows, or there are clouds in your data, histogram
equalization may not be appropriate.

Histogram Matching
 It is the process of determining a look up table that will
convert the histogram of one image to resemble the
histogram of the other.
 Histogram matching is useful for matching data of the same
or adjacent scenes that were scanned on separate dates, or are
slightly different because of sun angle or atmospheric effects.
 This is specially useful for mosaicking or change detection.
 To achieve good results with histogram matching the two
input images should have similar characterstics.
1. General shape of the histogram curves should be
similar.
2. Relative dark and light features in the image should be
same.
3. For some applications, the spatial resolution of the data
should be the same.
4. The relative distributions of the landcovers should be
the same.
5. Clouds should be removed before matching the
histogram.

Percentage Linear Contrast Stretch